def train_dialogue(domain_file='weather_domain.yml',
model_path='./models/dialogue',
training_data_file='./data/stories.md'):
agent = Agent(domain_file, policies=[MemoizationPolicy(), KerasPolicy()])
agent.train(training_data_file,
epochs=300,
batch_size=50,
validation_split=0.2)
agent.persist(model_path)
return agent
def train_dialog(domain_file="domain.yml",
model_path="models/dialogue",
training_data_file="data/stories.md"):
agent = Agent(domain_file, policies=[MemoizationPolicy(), EventPolicy()])
agent.train(training_data_file,
max_history=3,
epochs=400,
batch_size=100,
validation_split=0.2)
agent.persist(model_path)
return agent
def train_dialogue(domain_file="weather_domain.yml",
model_path="models/dialogue",
training_data_file="data/babi_stories.md"):
agent = Agent(domain_file, policies=[MemoizationPolicy(), WeatherPolicy()])
agent.train(training_data_file,
max_history=3,
epochs=2000,
batch_size=50,
augmentation_factor=50,
validation_split=0.2)
agent.persist(model_path)
return agent
def learnonline(self, msg, args):
"""Command to trigger learn_online on rasa agent"""
token = config.BOT_IDENTITY['token']
if token is None:
raise Exception('No slack token')
train_agent= Agent(self.domain_file,
policies=[MemoizationPolicy(max_history=2), KerasPolicy()],
interpreter=RegexInterpreter())
training_data = train_agent.load_data(self.training_data_file)
train_agent.train_online(training_data,
input_channel=self.backend_adapter,
batch_size=50,
epochs=200,
max_training_samples=300)
def train_dialogue(domain_file='../mom/domain.yml',
model_path='../models/policy/mom',
training_data_file="../mom/data/stories.md"):
agent = Agent(domain_file, policies=[MemoizationPolicy(), MomPolicy()])
agent.train(training_data_file,
max_history=3,
epochs=100,
batch_size=50,
augmentation_factor=50,
validation_split=0.2)
agent.persist(model_path)
return agent
def train_dialogue(domain_file='domain.yml',
model_path='./models/current/dialogue',
training_data_file='./data/stories.md'):
agent = Agent(domain_file,
policies=[
MemoizationPolicy(),
KerasPolicy(max_history=3, epochs=200, batch_size=50)
])
data = agent.load_data(training_data_file)
agent.train(data)
agent.persist(model_path)
return agent
